docstr: _make_layer.
  :param block: torch.nn.Module, class of blocks.
  :param planes: int,  number of channels produced by the convolution.
  :param blocks: int, number of layers in a block.
  :param stride: int, size of the convolving kernel.
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code: def _make_layer(self, block, planes, blocks, stride=1):
      """_make_layer.
      :param block: torch.nn.Module, class of blocks.
      :param planes: int,  number of channels produced by the convolution.
      :param blocks: int, number of layers in a block.
      :param stride: int, size of the convolving kernel.
      """
      downsample = None
      if stride != 1 or self.inplanes != planes * block.expansion:
          downsample = self.downsample_block(
              inplanes=self.inplanes,
              outplanes=planes * block.expansion,
              stride=stride,
          )
  
      layers = []
      layers.append(
          block(
              self.inplanes,
              planes,
              stride,
              downsample,
              relu_type=self.relu_type,
          )
      )
      self.inplanes = planes * block.expansion
      for _ in range(1, blocks):
          layers.append(
              block(
                  self.inplanes,
                  planes,
                  relu_type=self.relu_type,
              )
          )
  
      return nn.Sequential(*layers)
  
file_path: audio/examples/avsr/models/resnet.py
imports: import torch.nn
label_desc: Use image node_id 2 for calling the ResNet obj's underlying member method code with example usage: obj._make_layer(block, planes, blocks, stride) and returns: nn